





'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase									=   logging.get_logger(__name__)
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["input_features"]



	def __init__( self		,   a_=80		,   a_=16_000		,   a_=160		,   a_=30		,   a_=400		,   a_=0.0		,   a_=False		,   **a_		,   ):
			super().__init__(
			    feature_size=a_		,   sampling_rate=a_		,   padding_value=a_		,   return_attention_mask=a_		,   **a_		,   )
			lowerCAmelCase  :     List[str]       						= n_fft
			lowerCAmelCase  :     Optional[int]       						= hop_length
			lowerCAmelCase  :     List[str]       						= chunk_length
			lowerCAmelCase  :     Tuple       						= chunk_length * sampling_rate
			lowerCAmelCase  :     str       						= self.n_samples // hop_length
			lowerCAmelCase  :     Optional[int]       						= sampling_rate
			lowerCAmelCase  :     Optional[int]       						= mel_filter_bank(
			    num_frequency_bins=1 + n_fft // 2		,   num_mel_filters=a_		,   min_frequency=0.0		,   max_frequency=8000.0		,   sampling_rate=a_		,   norm="slaney"		,   mel_scale="slaney"		,   )



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Tuple       						= spectrogram(
			    a_		,   window_function(self.n_fft		,   "hann"						)		,   frame_length=self.n_fft		,   hop_length=self.hop_length		,   power=2.0		,   mel_filters=self.mel_filters		,   log_mel="log10"		,   )
			lowerCAmelCase  :     List[str]       						= log_spec[:, :-1]
			lowerCAmelCase  :     Tuple       						= np.maximum(a_		,   log_spec.max() - 8.0						)
			lowerCAmelCase  :     Tuple       						= (log_spec + 4.0) / 4.0
			return log_spec



	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def 	_lowerCamelCase     ( a_		,   a_		,   a_ = 0.0						):
			if attention_mask is not None:
					lowerCAmelCase  :     List[Any]       						= np.array(a_		,   np.intaa						)
					lowerCAmelCase  :     Optional[int]       						= []

					for vector, length in zip(a_		,   attention_mask.sum(-1						)						):
							lowerCAmelCase  :     str       						= (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
							if length < normed_slice.shape[0]:
									lowerCAmelCase  :     Optional[Any]       						= padding_value

							normed_input_values.append(a_						)
			else:
					lowerCAmelCase  :     Optional[Any]       						= [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

			return normed_input_values



	def __call__( self		,   a_		,   a_ = True		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = "max_length"		,   a_ = None		,   a_ = None		,   a_ = None		,   **a_		,   ):

			if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
							raise ValueError(
							    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
							    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
							    F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''						)
			else:
					logger.warning(
					    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
					    "Failing to do so can result in silent errors that might be hard to debug."						)

			lowerCAmelCase  :     str       						= isinstance(a_		,   np.ndarray						) and len(raw_speech.shape						) > 1
			if is_batched_numpy and len(raw_speech.shape						) > 2:
					raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''						)
			lowerCAmelCase  :     List[str]       						= is_batched_numpy or (
			    isinstance(a_		,   (list, tuple)						) and (isinstance(raw_speech[0]		,   (np.ndarray, tuple, list)						))
			)

			if is_batched:
					lowerCAmelCase  :     Optional[int]       						= [np.asarray([speech]		,   dtype=np.floataa						).T for speech in raw_speech]
			elif not is_batched and not isinstance(a_		,   np.ndarray						):
					lowerCAmelCase  :     Any       						= np.asarray(a_		,   dtype=np.floataa						)
			elif isinstance(a_		,   np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
					lowerCAmelCase  :     Any       						= raw_speech.astype(np.floataa						)

			# always return batch
			if not is_batched:
					lowerCAmelCase  :     List[Any]       						= [np.asarray([raw_speech]						).T]

			lowerCAmelCase  :     Dict       						= BatchFeature({"input_features": raw_speech}						)

			# convert into correct format for padding

			lowerCAmelCase  :     Optional[int]       						= self.pad(
			    a_		,   padding=a_		,   max_length=max_length if max_length else self.n_samples		,   truncation=a_		,   pad_to_multiple_of=a_		,   return_attention_mask=return_attention_mask or do_normalize		,   )

			# zero-mean and unit-variance normalization
			if do_normalize:
					lowerCAmelCase  :     str       						= self.zero_mean_unit_var_norm(
					    padded_inputs["input_features"]		,   attention_mask=padded_inputs["attention_mask"]		,   padding_value=self.padding_value		,   )
					lowerCAmelCase  :     Tuple       						= np.stack(padded_inputs["input_features"]		,   axis=0						)

			# make sure list is in array format
			lowerCAmelCase  :     Union[str, Any]       						= padded_inputs.get("input_features"						).transpose(2		,   0		,   1						)

			lowerCAmelCase  :     str       						= [self._np_extract_fbank_features(a_						) for waveform in input_features[0]]

			if isinstance(input_features[0]		,   a_						):
					lowerCAmelCase  :     List[str]       						= [np.asarray(a_		,   dtype=np.floataa						) for feature in input_features]
			else:
					lowerCAmelCase  :     Union[str, Any]       						= input_features

			if return_attention_mask:
					# rescale from sample (48000) to feature (3000)
					lowerCAmelCase  :     Tuple       						= padded_inputs["attention_mask"][:, :: self.hop_length]

			if return_tensors is not None:
					lowerCAmelCase  :     int       						= padded_inputs.convert_to_tensors(a_						)

			return padded_inputs





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= copy.deepcopy(self.__dict__						)
			lowerCAmelCase  :     Tuple       						= self.__class__.__name__
			if "mel_filters" in output:
					del output["mel_filters"]
			return output










'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



